Outo Eguchi style trained on Anima Base 1.0. Also known as eguchi saan. Anima does know the artist already but it's very inconsistent, so I trained this.Trigger: @outoeguchiNote: Pupils may generate randomly. If you don't want pupils to ever show up, either put "pupils" in the negative prompt or "no pupils" in the positive prompt.The artist almost exclusively draws simple backgrounds, so the Lora is a little overfit on simpler/less detailed backgrounds. Not much can be done about it, unfortunately.

Outo Eguchi Style (Anima)

Training parameters (diffusion-pipe):<pre><code>output_dir = 'output'
dataset = 'dataset.toml'

epochs = 1000
max_steps = 500
micro_batch_size_per_gpu = 4
pipeline_stages = 1
gradient_accumulation_steps = 1
gradient_clipping = 1.0

warmup_steps = 50
lr_scheduler = 'constant' 
compile = true
# rex_cycle_steps = 1000 
save_every_n_steps = 50
#checkpoint_every_n_minutes = 120
activation_checkpointing = 'unsloth'
#activation_checkpointing = true
save_dtype = 'bfloat16'
caching_batch_size = 1

[model]
type = 'anima'
transformer_path = 'anima-preview3.safetensors'
vae_path = 'qwen_image_vae.safetensors'
qwen_path = 'Qwen3-0.6B'
dtype = 'bfloat16'
timestep_sample_method = 'logit_normal'
sigmoid_scale = 1.0
llm_adapter_lr = 0

cache_text_embeddings = false
shuffle_tags = true
caption_mode = "tags"
tag_delimiter = ', '
debug_caption_processing = false
qwen_nf4 = false

[adapter]
type = 'lora'
rank = 16
dtype = 'bfloat16'

[optimizer]
type = 'came'
lr = 2e-5
weight_decay = 0.05</code></pre>Dataset config:<pre><code>resolutions = [1024]

enable_ar_bucket = true

min_ar = 0.5
max_ar = 2.0
num_ar_buckets = 7

[[directory]]
path = '1_eguchisaan'
num_repeats = 2</code></pre>

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